User talk:Kithira/Course Pages/CSCI 12/Assignment 2/Group 5/Homework 4

The technique you guys implemented was very similar to that of ours. Using standard deviation was a good choice to identity outliers in the data. Our main concern exists in your decision to look at the minute data points for spikes. The question is should you mark a whole minute of high average activity as a spike? Let's perhaps imagine that our test subject had to run to class one day. If the run only lasted one minute it is possible that it would be filtered out by your choice of filter, and this could be important data to the study. Furthermore, sometimes a single minute of activity would have several oscillating spikes within its scope. By averaging over minutes, these spikes could easily average each other out, making one minute data point seem "normal" even if it contained many spikes. Perhaps looking instead at the seconds to find spikes could be more practical. Otherwise your filtering seems to be a very good method. One thing that you guys did really well is the concept of the upward trend and not marking spikes which are part of those trends.

Another thing that could be made clearer is the scale for the intensity classification; the scale provided here is different than the one specified in the handout and we were curious how your group determined the scale for the intensity classification. — Preceding unsigned comment added by 137.165.8.36 (talk) 15:40, 23 January 2013 (UTC)